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The previous chapters have exposed you to some very sophisticated methods in predictive analytics (e.g., lag variables for time-series analysis and ensembles of models).
This is one of the most common approaches for generating spatial lag variables [ 40, 55].
Moreover, we constructed two separate spatial lag variables for output and efficiency level of districts.
Spill over effects were captured through spatial lag variables with respect to outputs and efficiency distribution.
We used Akaike's information criterion (AIC) and Bayesian information criterion (BIC) to select significant lag variables and interventions.
Keeping with the practice, we also used identical spatial weighting matrix for both of our lag variables [ 45, 46].
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We added a weighted ethnic spatial lag variable and compare this to the conventional model.
5Recent studies have also controlled for the spatial lag variable as endogenous variable (e.g. Goetzke and Andrade [2009]).
Table 3 shows the results for the region-specific fixed effects model with the time dependent lag variable.
Furthermore, the weighted ethnic spatial lag variable we recommend includes consideration of other immigrants' choices and location, not just neighbourhood.
The first model specification presents estimates from a simple OLS regression model, while the second includes the spatial lag variable.
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